Comprehensive review on twin support vector machines
Twin support vector machine (TWSVM) and twin support vector regression (TSVR) are newly
emerging efficient machine learning techniques which offer promising solutions for …
emerging efficient machine learning techniques which offer promising solutions for …
Diagnosis of Alzheimer's disease using universum support vector machine based recursive feature elimination (USVM-RFE)
Alzheimer's disease is one of the most common causes of death in today's world. Magnetic
resonance imaging (MRI) provides an efficient and non-invasive approach for diagnosis of …
resonance imaging (MRI) provides an efficient and non-invasive approach for diagnosis of …
Inverse free reduced universum twin support vector machine for imbalanced data classification
Imbalanced datasets are prominent in real-world problems. In such problems, the data
samples in one class are significantly higher than in the other classes, even though the other …
samples in one class are significantly higher than in the other classes, even though the other …
KNN weighted reduced universum twin SVM for class imbalance learning
In real world problems, imbalance of data samples poses major challenge for the
classification problems as the data samples of a particular class are dominating. Problems …
classification problems as the data samples of a particular class are dominating. Problems …
Universum based Lagrangian twin bounded support vector machine to classify EEG signals
B Kumar, D Gupta - Computer methods and programs in biomedicine, 2021 - Elsevier
Background and objective The detection of brain-related problems and neurological
disorders like epilepsy, sleep disorder, and so on is done by using electroencephalogram …
disorders like epilepsy, sleep disorder, and so on is done by using electroencephalogram …
A Novel Dual-Center-Based Intuitionistic Fuzzy Twin Bounded Large Margin Distribution Machines
Intuitionistic fuzzy (IF) set theory combined with twin support vector machines (TSVM) has
shown highly advantageous performance in robust and fast classification. However, the …
shown highly advantageous performance in robust and fast classification. However, the …
EEG signal classification using a novel universum-based twin parametric-margin support vector machine
The Universum data, which indicates a sample that does not belong to any of the classes,
has been proved to be useful in supervised learning. The researchers have explored the …
has been proved to be useful in supervised learning. The researchers have explored the …
Robust twin bounded support vector machines for outliers and imbalanced data
Truncated loss functions are robust to class noise and outliers. A robust twin bounded
support vector machine is proposed in this paper that truncates the growth of its loss …
support vector machine is proposed in this paper that truncates the growth of its loss …
Voxel-based 3D face reconstruction and its application to face recognition using sequential deep learning
In this paper, a novel 3D face reconstruction technique is proposed along with a sequential
deep learning-based framework for face recognition. It uses the voxels generated from the …
deep learning-based framework for face recognition. It uses the voxels generated from the …
Functional iterative approach for Universum-based primal twin bounded support vector machine to EEG classification (FUPTBSVM)
Due to the increasing popularity of support vector machine (SVM) and the introduction of
Universum, many variants of SVM along with Universum such as Universum support vector …
Universum, many variants of SVM along with Universum such as Universum support vector …